Case Report: A Chinese Family of Woodhouse-Sakati Syndrome With Diabetes Mellitus, With a Novel Biallelic Deletion Mutation of the DCAF17 Gene

Prior biological knowledge and phenotype information may help to identify disease genes from human whole-genome and whole-exome sequencing studies. We developed Phenolyzer (http://phenolyzer.usc.edu), a tool that uses prior information to implicate genes involved in diseases. Phenolyzer exhibits superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text.

Advances in high-throughput DNA sequencing have revolutionized the discovery of variants in the human genome; however, interpreting the phenotypic effects of those variants is still a challenge. While several computational approaches to predict variant impact are available, their accuracy is limited and further improvement is needed. Here, we introduce ClinPred, an efficient tool for identifying disease-relevant nonsynonymous variants. Our predictor incorporates two machine learning algorithms that use existing pathogenicity scores and, notably, benefits from inclusion of normal population allele frequency from the gnomAD database as an input feature. Another major strength of our approach is the use of ClinVar—a rapidly growing database that allows selection of confidently annotated disease-causing variants—as a training set. Compared to other methods, ClinPred showed superior accuracy for predicting pathogenicity, achieving the highest area under the curve (AUC) score and increasing both the specificity and sensitivity in different test datasets. It also obtained the best performance according to various other metrics. Moreover, ClinPred performance remained robust with respect to disease type (cancer or rare disease) and mechanism (gain or loss of function). Importantly, we observed that adding allele frequency as a predictive feature—as opposed to setting fixed allele frequency cutoffs—boosts the performance of prediction. We provide pre-computed ClinPred scores for all possible human missense variants in the exome to facilitate its use by the community.

Hypogonadism, alopecia, diabetes mellitus, mental retardation, and extrapyramidal syndrome (also referenced as Woodhouse-Sakati syndrome) is a rare autosomal recessive multisystemic disorder. We have identified a founder mutation consisting of a single base-pair deletion in C2orf37 in eight families of Saudi origin. Three other loss-of-function mutations were subsequently discovered in patients of different ethnicities. The gene encodes a nucleolar protein of unknown function, and the cellular phenotype observed in patient lymphoblasts implicates a role for the nucleolus in the pathogenesis of this disease. Our findings expand the list of human disorders linked to the nucleolus and further highlight the developmental and/or maintenance functions of this organelle.